TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning
Dongming Wu, Jiahao Chang, Fan Jia, Yingfei Liu, Tiancai Wang,, Jianbing Shen
TL;DR
TopoMLP introduces a simple, high-performance pipeline for topology reasoning in autonomous driving, leveraging improved detection models and MLP-based topology heads to achieve state-of-the-art results.
Contribution
It presents a novel pipeline combining advanced detection models with MLP heads for topology reasoning, setting new benchmarks in autonomous driving scene understanding.
Findings
Achieves 41.2% OLS on OpenLane-V2 with ResNet-50
First solution for OpenLane Topology Challenge
Demonstrates simple yet effective topology reasoning pipeline
Abstract
Topology reasoning aims to comprehensively understand road scenes and present drivable routes in autonomous driving. It requires detecting road centerlines (lane) and traffic elements, further reasoning their topology relationship, i.e., lane-lane topology, and lane-traffic topology. In this work, we first present that the topology score relies heavily on detection performance on lane and traffic elements. Therefore, we introduce a powerful 3D lane detector and an improved 2D traffic element detector to extend the upper limit of topology performance. Further, we propose TopoMLP, a simple yet high-performance pipeline for driving topology reasoning. Based on the impressive detection performance, we develop two simple MLP-based heads for topology generation. TopoMLP achieves state-of-the-art performance on OpenLane-V2 benchmark, i.e., 41.2% OLS with ResNet-50 backbone. It is also the 1st…
Peer Reviews
Decision·ICLR 2024 poster
The proposed model improves the STOA on OpenLane dataset by refining its various components. The proposed enhanced TOP score also shed lights to a more accurate evaluation.
It is hard to make sense of why the TOP numbers are quite low, especially lane-lane topology. It would be good to add what is an acceptable number for robust driving. There is not a lot of discussion of what is still not solvable, to allow readers to understand the shortcomings of the methods. This would add discussions on what is the next steps to take.
In general the paper carries some good merits, including: + the paper is easy to follow. The contributions are very clear: good detection increases the topology reasoning. Based on the detection module, using two simple MLP heads for topology generation could win among other approaches. + this work derives from the participation of this year's Autonmous Driving Challenge. This is the first entry solution, as shown in Table 1 and Table 2. Authors find a problematic on the evalution metric of OL
The paper has some obvious shortcomings as follows. - Motivation / technical novelty seems to be limitted. Following the second paragrah in the introduction, it is empirically found that topology performance is improved with stronger detection. Any insight behind this? There seems not too many discussions on it. - Some ablations and experiments need to be discussed. Overall this work falls into the borderline category without too many novelty or insight. However, given (a) the topology reaso
1. The authors conduct a comprehensive investigation into the significance of detection performance in topology reasoning, utilizing the OpenLane-V2 metrics. They highlight that with a detection score of 28.3, the topology score can only reach a maximum of 7.5, even if the topology reasoning results are identical to the ground truth. 2. In the above scenario, their model achieves a topology score of 7.2. This demonstrates the model's superior performance in topology reasoning compared to other m
1. Although the overall performance of TopoMLP is impressive, it primarily stems from the overall framework and training strategy, where the novelty being somewhat limited. 2. The several modules proposed in the paper to enhance performance are effective, but they are quite straightforward and intuitive, lacking more refined and intricate design. 3. Some parts of the manuscript are unclear and could be further improved; errors and suggestions for improvement are detailed later in the Questions.
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Taxonomy
TopicsAdvanced Neural Network Applications · Human Pose and Action Recognition · Autonomous Vehicle Technology and Safety